Using selected examples from the fields of disinfection, antiseptics, perioperative antibiotic prophylaxis, disposable medical gloves, workwear and protective clothing, air conditioning systems, architectural modifications and recycling of recyclable medical products used in surgery, it is shown that the carbon footprint of inpatient and outpatient surgical facilities can be reduced by implementing infection control measures appropriate to the indications without compromising the safety of patients and staff.
Tibial plateau fractures are associated with a range of meniscal and ligamentous injuries that are relevant to the outcome. Standard diagnostics include radiographs and computed tomography (CT). Magnetic resonance imaging (MRI) is not routinely recommended, although it visualizes meniscal and ligamentous injuries with high sensitivity and specificity. The aim of the study was to analyze tibial plateau fractures for meniscal and ligamentous injuries and to determine whether predictability could be achieved using CT. The study also examined whether the detection of meniscal and ligamentous injuries depended on the experience of the trauma surgeon. Initially, 30 CT scans and subsequently 30 MRIs were evaluated by three residents and three consultants. To prevent rater recall of MRI–CT findings for a given patient, findings were presented in a randomized order with a two-week interval between CT and MRI assessments. A standardized questionnaire was used to evaluate soft-tissue injuries, fracture classification, surgical strategy, and the treatment of associated meniscal and ligamentous injuries for each CT and MRI. The radiologists’ MRI report was defined as the reference standard. The incidence of meniscal and ligamentous injuries associated with tibial plateau fractures was 93
Sex and gender significantly influence symptom presentation, risk perception, diagnostic pathways, and therapeutic decisions in emergency medicine. In time-critical and high-stress settings, these differences are often insufficiently recognized, leading to relevant disparities in care. Women, in particular, present more frequently with nonclassical or unspecific symptoms in acute conditions such as acute coronary syndrome, stroke, sepsis, trauma, and acute pain, resulting in delayed diagnosis and treatment despite comparable or higher morbidity and mortality.Furthermore, cognitive heuristics and implicit bias play a key role in shaping clinical decision-making under time pressure. These biases are not primarily a result of individual misconduct but rather reflect structural and systemic limitations within emergency care algorithms, medical education, and clinical research.This article outlines the current evidence on gender-related differences in emergency medicine and highlights clinically relevant bias mechanisms. Furthermore, it introduces the concept of Precision Emergency Medicine as a framework that integrates sex- and gender-specific factors into standardized emergency care pathways. Practical recommendations are provided to support gender-sensitive clinical perception, communication, documentation, and handover in the prehospital and acute care setting.Gender-sensitive emergency medicine is not an optional add-on but a core component of high-quality, safe, and equitable emergency care. Recognizing and addressing gender-related differences enables more precise clinical decision-making and improves patient safety under time pressure.
Substantial evidence has emerged supporting the use of telemonitoring for patients with heart failure (HF) [1-3]. However, the implementation of new digital care models in healthcare is progressing slowly due to various barriers, such as technological concerns, staff shortages and time constraints [4]. Furthermore, experience from telemedicine studies shows that a considerable proportion of the daily transmitted telemonitoring data contains findings that are clinically unremarkable. The primary hypothesis of this study is that an AI-based algorithm for daily risk detection will accurately identify HF patients requiring intervention in a post-hoc analysis from a cohort participating in a telemedicine care programme. The Telemedical Interventional Management in Heart Failure 3 (TIM-HF3) study is a new multicentre cohort study designed to generate data for the retrospective validation of an AI-based algorithm. Telemedical care was provided as part of standard care in accordance with Germany´s quality-assurance agreement for HF telemonitoring. In addition to routine parameters - daily transfer of blood pressure, 2-lead ECG, body weight and self-assessment- participants also measured peripheral oxygen saturation daily and recorded standardised voice samples weekly. A six-minute activity test and a quality of life questionnaire (PROMIS) were carried out during the baseline visit (BV) and the final visit (FV). Follow-up ranged from 6 to 18 months with BV conducted onsite and the FV via telemedicine. Recruitment took place from March 2023 to March 2024. All hospitalisations were adjudicated by an independent Endpoint Committee. The retrospectively used AI-algorithm, previously described in detail [5], was trained on data of the TIM-HF2 study. To maximize applicability, we applied identical inclusion and exclusion criteria (see picture 1). The algorithm was trained to predict unplanned HF hospitalisations within the following seven days. Baseline risk for each patient was calculated from BV-parameters. Thereafter, a daily risk score indicating the probability of HF hospitalisation within the next seven days was computed from the telemonitoring data. Only patients in the top-risk deciles were flagged for medical assessment by telemedical physicians (see picture 2). From these flags, we derived the sensitivity and specificity for detecting both telemedical interventions and hospitalisations. The goal was to ensure that ≥95% of patients who were hospitalised due to HF were reviewed in the week before the event, despite assessing only ≈30% of patients on any given day. AI-based algorithms can efficiently filter telemonitoring data to identify patients who need intervention, thereby improving patient care and conserving clinical resources. Nevertheless, integrating AI into routine care demands robust scientific validation and careful implementation planning.Inclusion and exclusion criteriaAI-based selection of patients
